1. Field of the Invention
The invention generally relates to sensor systems, and more particularly to systems and methods of attaining data fusion from a wireless unattended ground sensor network.
2. Description of the Related Art
Within this application several publications are referenced by Arabic numerals within brackets. Full citations for these, and other, publications may be found at the end of the specification immediately preceding the claims. The disclosures of all these publications in their entireties are hereby expressly incorporated by reference into the present application for the purposes of indicating the background of the present invention and illustrating the state of the art.
Monitoring a physical region by spatially distributing sensors within it is a topic of much current interest. This topic encompasses many applications, including that of a wireless unattended ground sensor (UGS) system. Essentially, such a system is a network consisting of spatially distributed nodes each of which typically contains a suite of multiple sensors, at least one digital signal processor (DSP) or central processing unit (CPU), and means for exfiltrating the processed data. Of more particular emphasis are multi-modal sensor suites. Such UGS networks are typically used for target detection, localization/tracking, and classification. Conventionally, the development of the classification for individual sensing modalities, as exemplified, for example, by acoustic modality[1] applications and imaging modality[2] applications has been an ongoing process.
In a wireless UGS system, each node's power source is self-contained, hence, maximizing network lifetime requires that power consumption be minimized. Whatever form of node level classification is used, it must be a low complexity process so that it can be carried out in real time with minimum power consumption (proportional to CPU clock speed) and minimum CPU unit cost. One also needs to limit the peak amount of online random access memory (RAM) storage that is required of a given node. However, minimizing read only memory (ROM) storage of offline, preprocessed data is of lower priority. Limits on node power, processing capability, and communication bandwidth have already motivated the development of low complexity algorithms for target detection and localization[3-5]. The ongoing Basic Orthogonal Sensor System (BOSS) program[4] is also concerned with low complexity classification at the node level.
Performance and energy efficiency of a UGS system is usually enhanced by the use of collaborative signal processing (CSP) at various levels of the network[3]. For low power, low cost UGS networks, target classification should be implemented as intrasensor collaboration[3] in the form of node multi-modal fusion. In fact, conventional work in this area suggests that one of the simplest and most feasible forms of CSP is to combine the information across different sensing modalities at each node[3]. The information, which is to be combined resides at one node, and therefore does not require communication over the network[3]. Assuming that feature level fusion, such as level one fusion, is desired for the sake of accuracy and robustness, carrying it out at the node level greatly reduces the amount of data to be exfiltrated from a given node. The effect of this reduced bandwidth is that one must then classify targets at the node level using high dimensional feature vectors. These large number of features arise from the concatenation of the feature vectors from each of the sensor modalities that are to be fused[3]. Moreover, this feature-level concatenation approach to multi-modal fusion is prudent as it retains the use of the vast expertise gained in feature extraction development for various individual modalities.
It is acknowledged that successful classification is crucially dependent upon appropriate feature selection for the application at hand. In fact, node level classification for low power, low cost UGS networks requires the use of a low complexity classifier which is also capable of accurately distinguishing between (possibly many) classes in a high dimensional feature space.
However, there is a conflict between the requirement of low computational complexity and the need to classify in high dimensional feature spaces. The need for low computational complexity is even more acute if one implements node temporal fusion[6] at the level above multi-modal fusion, that is, before further classification refinement at the node cluster or network level. In such a case, multi-modal-fused classification would be required for each of a sequence of time windows with the resulting sequence of class predictions undergoing some form of information fusion (such as voting) before exfiltrating the result from the node. The most appropriate classifiers for this situation can hence be determined by considering the complexity of various popular classifiers as a function of feature dimension. A very popular classifier which is also considered to be of low query complexity is the Multivariate Gaussian Classifier (MVG)[7], also known as the quadratic classifier. In fact, currently the MVG classifier is a preferred choice for the United States Army Acoustic Tracking algorithm[8], and it is also one of those evaluated by others[3] for application at the node level in UGS (unattended ground sensor) systems.
One possible measure of the online computational complexity of a classifier is the number of floating point operations (flops) that is required to classify a query vector once the classifier itself has already been trained. In this case, the MVG classifier's complexity is O(Nd2), where O(Nd2) denotes “of the order of Nd2” asymptotically, where N is the number of classes and d is the dimension of the feature space (varies on a case-by-case basis). Due to this quadratic dependence on d, the MVG classifier is of low complexity for those cases for which d is not too large, but for large values of d it can rapidly become a high complexity classifier. The query complexity of the k-Nearest Neighbor (kNN), Nearest Neighbor (NN), and Parzen-window-based[7] classifiers, such as the Probabilistic Neural Network (PNN)[7], are all O(nTd), where nT is the size of the training dataset. This estimate neglects the nonlinear evaluations that are typical in Parzen-window-based classifiers. Although these classifiers are linear in their d dependence, the fact that nT is typically large usually precludes such classifiers from low query complexity consideration. Some conventional techniques[9, 10] strive to drastically reduce the query complexity of the NN and kNN methods. However, these traditional techniques may encounter difficulties as d increases. Brute force (such as where all data points are considered) NN/kNN is still a preferred technique[9] for most such efforts for d≧O(log nT).
As another classifier example, the query complexity of a nonlinear Support Vector Machine (SVM)[11, 12] is O(NnSVd), neglecting the nonlinear evaluations, where nSV is the number of support vectors required for the particular application. In contrast to this, the complexity of a linear classifier, including linear SVM classifiers, is only O(Nd). However, a nonlinear SVM classifier cannot compete with an MVG classifier in terms of query complexity alone unless nSV≦O(d). Furthermore, one can easily find realistic datasets for which nSV is not only appreciably larger than d, but it is even an appreciable fraction of nT[12]. As such, there are efforts to reduce the required number of support vectors[13]. Nevertheless, whether or not a nonlinear SVM is of low complexity appears to be application dependent.
As a final classifier example, the query complexity of a neural networks[7] with only one hidden layer (to minimize complexity) is O(nNd) for the hidden layer and O(NnN) for the output layer, where nN is the number of neurons in the hidden layer. Hence, such a neural net classifier can readily compete with an MVG classifier in terms of query complexity so long as nN(d)≦O(d) at least asymptotically for large values of d, that is, as long as the dependence of nN on d is sub-linear. The minimum value of nN for a given application is typically tied to classifier performance issues, whereas the maximum allowable value of nN can be determined by nT≧5nN so as to prevent over fitting[2].
This discussion regarding the d-dependence of the complexity of some existing classifiers begs the question as to whether additional classifiers can be developed which are sub-quadratic overall in their query complexity dependence on d. Increasing the “toolbox” of such sub-quadratic-d-complexity algorithms will be of benefit in at least two ways. It will allow one greater choice as to which such classifier is more appropriate to a particular application. Just as importantly, however, it will also increase the opportunity for building other low complexity classifiers from the fusion of low complexity classifiers already available in the “toolbox”.
Therefore, due to the limitations of the conventional systems and methods, there is a need for a novel fusion process for the construction of such new classifiers from existing low complexity boundary-decision classifiers as well as a system for implementing such a process.